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<title>Machine Learning Papers and Abstracts</title>

<h1>Machine Learning Papers and Abstracts</h1>

To view a paper, click on the open book image. <br> 
<br>


<ol>



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<a name="chill-ilp-96.ps.Z"</a>

<b><li>Inductive Logic Programming for Natural Language Processing</b>
Raymond J. Mooney<br>

<cite>Proceedings of the 6th International Inductive Logic Programming Workshop</cite>, pp. 205-224, Stockholm, Sweden, August 1996. <p>

<blockquote>
This paper reviews our recent work on applying inductive logic
programming to the construction of natural language processing
systems. We have developed a system, CHILL, that learns a parser from
a training corpus of parsed sentences by inducing heuristics that
control an initial overly-general shift-reduce parser.  CHILL learns
syntactic parsers as well as ones that translate English database
queries directly into executable logical form.  The ATIS corpus of
airline information queries was used to test the acquisition of
syntactic parsers, and CHILL performed competitively with recent
statistical methods.  English queries to a small database on
U.S. geography were used to test the acquisition of a complete natural
language interface, and the parser that CHILL acquired was more
accurate than an existing hand-coded system.  The paper also includes
a discussion of several issues this work has raised regarding the
capabilities and testing of ILP systems as well as a summary of our
current research directions.
</blockquote>

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<a name="rapture-dissertation-96.ps.Z"</a>

<b><li>Combining Symbolic and Connectionist Learning Methods to Refine
Certainty-Factor Rule-Bases<br></b>
J. Jeffrey Mahoney<br>

Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, May, 1996.<p>

<blockquote>
This research describes the system RAPTURE, which is designed to
revise rule bases expressed in certainty-factor format.  Recent
studies have shown that learning is facilitated when biased with
domain-specific expertise, and have also shown that many real-world
domains require some form of probabilistic or uncertain reasoning in
order to successfully represent target concepts. RAPTURE was designed
to take advantage of both of these results. <p>

Beginning with a set of certainty-factor rules, along with
accurately-labelled training examples, RAPTURE makes use of both
symbolic and connectionist learning techniques for revising the rules,
in order that they correctly classify all of the training examples. A
modified version of backpropagation is used to adjust the certainty
factors of the rules, ID3's information-gain heuristic is used to add
new rules, and the Upstart algorithm is used to create new hidden
terms in the rule base. <p>

Results on refining four real-world rule bases are presented that
demonstrate the effectiveness of this combined approach.  Two of these
rule bases were designed to identify particular areas in strands of
DNA, one is for identifying infectious diseases, and the fourth
attempts to diagnose soybean diseases.  The results of RAPTURE are
compared with those of backpropagation, C4.5, KBANN, and other
learning systems.  RAPTURE generally produces sets of rules that are
more accurate that these other systems, often creating smaller sets of
rules and using less training time. <p>

</blockquote>

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<a name="scope-msl-96.ps.Z"</a>

<b><li> Integrating EBL and ILP to Acquire Control Rules for Planning<br></b>
Tara A. Estlin and Raymond J. Mooney<br>

<cite>Proceedings of the Third International Workshop on
Multi-Strategy Learning</cite>, pp. 271-279, Harpers Ferry, WV, May
1996. (MSL-96).<p>

<blockquote>
Most approaches to learning control information in planning systems
use explanation-based learning to generate control rules.
Unfortunately, EBL alone often produces overly complex rules that
actually decrease planning efficiency.  This paper presents a novel
learning approach for control knowledge acquisition that integrates
explanation-based learning with techniques from inductive logic
programming. EBL is used to constrain an inductive search for
selection heuristics that help a planner choose between competing plan
refinements. SCOPE is one of the few systems to address learning
control information in the newer partial-order planners.
Specifically, SCOPE learns domain-specific control rules for a version
of the UCPOP planning algorithm. The resulting system is shown to
produce significant speedup in two different planning domains.
</blockquote>

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<a name="emnlp-96.ps.Z"</a>

<b><li>Comparative Experiments on Disambiguating Word Senses:
An Illustration of the Role of Bias in Machine Learning
<br></b>
Raymond J. Mooney<br>

<cite>Proceedings of the 1996 Conference on Empirical Methods in
Natural Language Processing</cite>, pp. 82-91, Philadelphia, PA, May
1996.. <p>

<blockquote>
This paper describes an experimental comparison of seven different learning
algorithms on the problem of learning to disambiguate the meaning of a word
from context.  The algorithms tested include statistical, neural-network,
decision-tree, rule-based, and case-based classification techniques.  The
specific problem tested involves disambiguating six senses of the word ``line''
using the words in the current and proceeding sentence as context.  The
statistical and neural-network methods perform the best on this particular
problem and we discuss a potential reason for this observed difference.  We
also discuss the role of bias in machine learning and its importance in
explaining performance differences observed on specific problems.
</blockquote>

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<a name="chill-aaai-96.ps.Z"</a>

<b><li>Learning to Parse Database Queries using Inductive Logic Programming<br></b>
John M. Zelle and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 1050-1055, Portland, OR, August, 1996. (AAAI-96)<p>

<blockquote>
This paper presents recent work using the CHILL parser acquisition
system to automate the construction of a natural-language interface
for database queries.  CHILL treats parser acquisition as the learning
of search-control rules within a logic program representing a
shift-reduce parser and uses techniques from Inductive Logic
Programming to learn relational control knowledge.  Starting with a
general framework for constructing a suitable logical form, CHILL is
able to train on a corpus comprising sentences paired with database
queries and induce parsers that map subsequent sentences directly into
executable queries.  Experimental results with a complete
database-query application for U.S. geography show that CHILL is able
to learn parsers that outperform a pre-existing, hand-crafted
counterpart.  These results demonstrate the ability of a corpus-based
system to produce more than purely syntactic representations.  They
also provide direct evidence of the utility of an empirical approach
at the level of a complete natural language application.
</blockquote>

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<a name="assert-aaai-96.ps.Z"</a>

<b><li>A Novel Application of Theory Refinement to Student Modeling<br></b>
Paul Baffes and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 403-408, Portland, OR, August, 1996. (AAAI-96)<p>

<blockquote>
Theory refinement systems developed in machine learning automatically
modify a knowledge base to render it consistent with a set of
classified training examples. We illustrate a novel application of
these techniques to the problem of constructing a student model for an
intelligent tutoring system (ITS). Our approach is implemented in an
ITS authoring system called Assert which uses theory refinement to
introduce errors into an initially correct knowledge base so that it
models incorrect student behavior. The efficacy of the approach has
been demonstrated by evaluating a tutor developed with Assert with 75
students tested on a classification task covering concepts from an
introductory course on the C++ programming language. The system
produced reasonably accurate models and students who received feedback
based on these models performed significantly better on a post test
than students who received simple reteaching.
</blockquote>

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<a name="qdocs-aaai-96.ps.Z"</a>

<b><li> Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using
Behavioral Modes<br></b>
Siddarth Subramanian and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 965-970, Portland, OR, August, 1996. (AAAI-96)<p>

<blockquote>
Most model-based diagnosis systems, such as GDE and Sherlock, have
concerned discrete, static systems such as logic circuits and use
simple constraint propagation to detect inconsistencies. However,
sophisticated systems such as QSIM and QPE have been developed for
qualitative modeling and simulation of continuous dynamic systems.  We
present an integration of these two lines of research as implemented
in a system called QDOCS for multiple-fault diagnosis of continuous
dynamic systems using QSIM models. The main contributions of the
algorithm include a method for propagating dependencies while solving
a general constraint satisfaction problem and a method for verifying
the consistency of a behavior with a model across time. Through
systematic experiments on two realistic engineering systems, we
demonstrate that QDOCS demonstrates the best balance of generality,
accuracy, and efficiency among competing methods.
</blockquote>

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<a name="scope-aaai-96.ps.Z"</a>

<b><li> Multi-Strategy Learning of Search Control for Partial-Order
Planning<br></b>
Tara A. Estlin and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 843-848, Portland, OR, August, 1996. (AAAI-96)<p>


<blockquote>
Most research in planning and learning has involved linear,
state-based planners. This paper presents SCOPE, a system for learning
search-control rules that improve the performance of a partial-order
planner.  SCOPE integrates explanation-based and inductive learning
techniques to acquire control rules for a partial-order planner.
Learned rules are in the form of selection heuristics that help the
planner choose between competing plan refinements.  Specifically,
SCOPE learns domain-specific control rules for a version of the UCPOP
planning algorithm. The resulting system is shown to produce
significant speedup in two different planning domains.
</blockquote>

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<a name="scope-proposal-96.ps.Z"</a>

<b><li> Integrating Explanation-Based and Inductive Learning Techniques
to Acquire Search-Control for Planning<br></b>
Tara A. Estlin<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1996. <p>

<blockquote> 
Planning systems have become an important tool for automating a wide
variety of tasks. Control knowledge guides a planner to find solutions
quickly and is crucial for efficient planning in most domains.
Machine learning techniques enable a planning system to automatically
acquire domain-specific search-control knowledge for different
applications.  Past approaches to learning control information have
usually employed explanation-based learning (EBL) to generate control
rules.  Unfortunately, EBL alone often produces overly complex rules
that actually decrease rather than improve overall planning
efficiency.  This paper presents a novel learning approach for control
knowledge acquisition that integrates explanation-based learning with
techniques from inductive logic programming. In our learning system
SCOPE, EBL is used to constrain an inductive search for control
heuristics that help a planner choose between competing plan
refinements. SCOPE is one of the few systems to address learning
control information for newer, partial-order planners.  Specifically,
this proposal describes how SCOPE learns domain-specific control rules
for the UCPOP planning algorithm. The resulting system is shown to
produce significant speedup in two different planning domains, and to
be more effective than a pure EBL approach.<p>

Future research will be performed in three main areas.  First, SCOPE's
learning algorithm will be extended to include additional techniques
such as constructive induction and rule utility analysis.  Second,
SCOPE will be more thoroughly tested; several real-world planning
domains have been identified as possible testbeds, and more in-depth
comparisons will be drawn between SCOPE and other competing
approaches.  Third, SCOPE will be implemented in a different planning
system in order to test its portability to other planning algorithms.
This work should demonstrate that machine-learning techniques can be a
powerful tool in the quest for tractable real-world planning.<p>
</blockquote>

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<a name="wolfie-ml-96.ps.Z"</a>

<b><li>Lexical Acquisition: A Novel Machine Learning Problem<br></b>
Cynthia A. Thompson and Raymond J. Mooney<br>

Technical Report, Artificial Intelligence Lab, 
University of Texas at Austin, 1996. <p>

<blockquote>
This paper defines a new machine learning problem to which standard
machine learning algorithms cannot easily be applied.  The problem
occurs in the domain of lexical acquisition. The ambiguous and
synonymous nature of words causes the difficulty of using standard
induction techniques to learn a lexicon.  Additionally, negative
examples are typically unavailable or difficult to construct in this
domain.  One approach to solve the lexical acquisition problem is
presented, along with preliminary experimental results on an
artificial corpus.  Future work includes extending the algorithm and
performing tests on a more realistic corpus.
</blockquote>

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<a name="foidl-ml-96.ps.Z"</a>

<b><li>Advantages of Decision Lists and Implicit Negative in Inductive Logic
Programming<br></b>
Mary Elaine Califf and Raymond J. Mooney<br>

Technical Report, Artificial Intelligence Lab, 
University of Texas at Austin, 1996. <p>

<blockquote>
This paper demonstrates the capabilities of FOIDL, an inductive logic
programming (ILP) system whose distinguishing characteristics are the
ability to produce first-order decision lists, the use of an output
completeness assumption to provide implicit negative examples, and the
use of intensional background knowledge.  The development of FOIDL was
originally motivated by the problem of learning to generate the past
tense of English verbs; however, this paper demonstrates its superior
performance on two different sets of benchmark ILP problems.  Tests on
the finite element mesh design problem show that FOIDL's decision
lists enable it to produce better results than all other ILP systems
whose results on this problem have been reported.  Tests with a selection
of list-processing problems from Bratko's introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allow
FOIDL to learn correct programs from far fewer examples than FOIL.
</blockquote>

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<a name="contex-acl-96.ps.Z"</a>

<b><li>Learning Parse Decisions From Examples With Rich Context<br></b>
Ulf Hermjakob and Raymond J. Mooney<br>

Submitted to the 34th Annual Meeting of the Association for Computational Linguistics (ACL-96).<p>

<blockquote>
We present a knowledge and context-based system for parsing natural language
and evaluate it on sentences from the Wall Street Journal.
Applying machine learning techniques, the system uses parse action examples
acquired under supervision to generate a deterministic shift-reduce parser
in the form of a decision structure. It relies heavily on context, as encoded 
in features which describe the morpholgical, syntactical, semantical and
other aspects of a given parse state.
</blockquote>

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<a name="banner-wolfie-95.ps.Z"</a>

<b><li> Corpus-Based Lexical Acquisition For Semantic Parsing <br></b>
Cynthia Thompson<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1995. <p>

<blockquote>
Building accurate and efficient natural language processing (NLP)
systems is an important and difficult problem.  There has been
increasing interest in automating this process.  The lexicon, or the
mapping from words to meanings, is one component that is typically
difficult to update and that changes from one domain to the next.
Therefore, automating the acquisition of the lexicon is an important
task in automating the acquisition of NLP systems.  This proposal
describes a system, WOLFIE (WOrd Learning From Interpreted Examples),
that learns a lexicon from input consisting of sentences paired with
representations of their meanings.  Preliminary experimental results
show that this system can learn correct and useful mappings.  The
correctness is evaluated by comparing a known lexicon to one learned
from the training input.  The usefulness is evaluated by examining the
effect of using the lexicon learned by WOLFIE to assist a parser
acquisition system, where previously this lexicon had to be
hand-built.  Future work in the form of extensions to the algorithm,
further evaluation, and possible applications is discussed.
</blockquote>

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<a name="banner-proposal-95.ps.Z"</a>

<b><li> Refinement of Bayesian Networks by Combining Connectionist and
Symbolic Techniques <br></b>
Sowmya Ramanchandran<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1995. <p>

<blockquote>
Bayesian networks provide a mathematically sound formalism for
representing and reasoning with uncertain knowledge and are as such
widely used. However, acquiring and capturing knowledge in this
framework is difficult. There is a growing interest in formulating
techniques for learning Bayesian networks inductively. While the
problem of learning a Bayesian network, given complete data, has been
explored in some depth, the problem of learning networks with
unobserved causes is still open. In this proposal, we view this
problem from the perspective of theory revision and present a novel
approach which adapts techniques developed for revising theories in
symbolic and connectionist representations.  Thus, we assume that the
learner is given an initial approximate network (usually obtained from
a expert). Our technique inductively revises the network to fit the
data better.  Our proposed system has two components: one component
revises the parameters of a Bayesian network of known structure, and
the other component revises the structure of the network. The
component for parameter revision maps the given Bayesian network into
a multi-layer feedforward neural network, with the parameters mapped
to weights in the neural network, and uses standard backpropagation
techniques to learn the weights. The structure revision component uses
qualitative analysis to suggest revisions to the network when it fails
to predict the data accurately. The first component has been
implemented and we will present results from experiments on real world
classification problems which show our technique to be effective.  We
will also discuss our proposed structure revision algorithm, our plans
for experiments to evaluate the system, as well as some extensions to
the system.
</blockquote>

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<a name="assert-jaied-95.ps.Z"</a>

<b><li> Refinement-Based Student Modeling and Automated Bug Library 
Construction<br></b>
Paul Baffes and Raymond Mooney<br>

<cite>Journal of Artificial Intelligence in Education</cite>, 7, 1
(1996), pp. 75-116.<p>


<blockquote>
A critical component of model-based intelligent tutoring sytems is a
mechanism for capturing the conceptual state of the student, which
enables the system to tailor its feedback to suit individual strengths
and weaknesses.  To be useful such a modeling technique must be
<em>practical</em>, in the sense that models are easy to construct, and
<em>effective</em>, in the sense that using the model actually impacts student
learning.  This research presents a new student modeling technique
which can automatically capture novel student errors using only
correct domain knowledge, and can automatically compile trends across
multiple student models.  This approach has been implemented as a
computer program, ASSERT, using a machine learning technique called
<em>theory refinement</em>, which is a method for automatically revising a
knowledge base to be consistent with a set of examples.  Using a
knowledge base that correctly defines a domain and examples of a
student's behavior in that domain, ASSERT models student errors by
collecting any refinements to the correct knowledege base which are
necessary to account for the student's behavior.  The efficacy of the
approach has been demonstrated by evaluating ASSERT using 100 students
tested on a classification task covering concepts from an introductory
course on the C++ programming language.  Students who received
feedback based on the models automatically generated by ASSERT
performed significantly better on a post test than students who
received simple teaching.
</blockquote>

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<a name="chill-bkchapter-95.ps.Z"</a>

<b><li>Comparative Results on Using Inductive Logic Programming
for Corpus-based Parser Construction<br></b>
John M. Zelle and Raymond J. Mooney<br>

<cite>Symbolic, Connectionist, and Statistical Approaches to Learning for Natural
Language Processing</cite>, S. Wermter, E. Riloff and G. Scheler, Eds,
Spring Verlag, 1996. <p> 

<blockquote>
This paper presents results from recent experiments with CHILL, a
corpus-based parser acquisition system.  CHILL treats language
acquisition as the learning of search-control rules within a logic
program.  Unlike many current corpus-based approaches that use
statistical learning algorithms, CHILL uses techniques from inductive
logic programming (ILP) to learn relational representations.  CHILL is
a very flexible system and has been used to learn parsers that produce
syntactic parse trees, case-role analyses, and executable database
queries.  The reported experiments compare CHILL's performance to that
of a more naive application of ILP to parser acquisition.  The results
show that ILP techniques, as employed in CHILL, are a viable
alternative to statistical methods and that the control-rule framework
is fundamental to CHILL's success.
</blockquote>

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<a name="foidl-bkchapter-95.ps.Z"</a>

<b><li>Learning the Past Tense of English Verbs Using Inductive Logic 
Programming<br></b>
Raymond J. Mooney and Mary Elaine Califf<br>

<cite>Symbolic, Connectionist, and Statistical Approaches to Learning for Natural
Language Processing</cite>, S. Wermter, E. Riloff and G. Scheler, Eds,
Spring Verlag, 1996. <p> 

<blockquote> 

This paper presents results on using a new inductive logic programming
method called FOIDL to learn the past tense of English verbs. The past
tense task has been widely studied in the context of the
symbolic/connectionist debate.  Previous papers have presented results
using various neural-network and decision-tree learning methods.  We
have developed a technique for learning a special type of Prolog
program called a <em>first-order decision list</em>, defined as an
ordered list of clauses each ending in a cut.  FOIDL is based on FOIL
(Quinlan, 1990) but employs intensional background knowledge and
avoids the need for explicit negative examples.  It is particularly
useful for problems that involve rules with specific exceptions, such
as the past-tense task.  We present results showing that FOIDL learns
a more accurate past-tense generator from significantly fewer examples
than all other previous methods.
</blockquote>

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<a name="dolphin-ewsp-95.ps.Z" </a>

<b> <li> Hybrid Learning of Search Control for Partial-Order Planning <br> </b>

Tara A. Estlin and Raymond J. Mooney <br>

<cite>New Directions in AI Planning</cite>, M. Ghallab and A. Milani, Eds,
IOS Press, 1996, pp. 129-140. <p>  

<blockquote>

This paper presents results on applying a version of the DOLPHIN
search-control learning system to speed up a partial-order planner.
DOLPHIN integrates explanation-based and inductive learning techniques
to acquire effective clause-selection rules for Prolog programs.  A
version of the UCPOP partial-order planning algorithm has been
implemented as a Prolog program and DOLPHIN used to automatically
learn domain-specific search control rules that help eliminate
backtracking. The resulting system is shown to produce significant
speedup in several planning domains.

</blockquote>

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<a name="banner-icnn-96.ps.Z"</a>


<b><li>Revising Bayesian Network Parameters Using Connectionist Methods<br></b>
Sowmya Ramachandran and Raymond J. Mooney<br>

<cite>Proceedings of the International Conference on Neural
Networks (ICNN-96)</cite>, Special Session on Knowledge-Based Artificial
Neural Networks, Washington DC, June 1996. <p>

<blockquote>
The problem of learning Bayesian networks with hidden variables is known to
be a hard problem. Even the simpler task of learning just the conditional
probabilities on a Bayesian network with hidden variables is hard. In this
paper, we present an approach that learns the conditional probabilities on
a Bayesian network with hidden variables by transforming it into a
multi-layer feedforward neural network (ANN). The conditional probabilities
are mapped onto weights in the ANN, which are then learned using standard
backpropagation techniques. To avoid the problem of exponentially large
ANNs, we focus on Bayesian networks with noisy-or and noisy-and
nodes. Experiments on real world classification problems demonstrate the
effectiveness of our technique.
</blockquote>

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<a name="qdocs-dissertation-95.ps.Z"</a>

<b><li>Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using
Behavioral Modes<br></b>

Siddarth Subramanian<br>

Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August, 1995.<p>

<blockquote>

As systems like chemical plants, power plants, and automobiles get
more complex, online diagnostic systems are becomingly increasingly
important.  One of the ways to rein in the complexity of describing
and reasoning about large systems such as these is to describe them
using qualitative rather than quantitative models.<p>

Model-based diagnosis is a class of diagnostic techniques that use
direct knowledge about how a system functions instead of expert rules
detailing causes for every possible set of symptons of a broken
system.  Our research builds on standard methods for model-based
diagnosis and extends them to the domain of complex dynamic systems
described using qualitative models.<p>

We motivate and describe out algorithm for diagnosing faults in a
dynamic system given a qualitative model and a sequence of qualitative
states.  The main contributions in this algorithm include a method for
propagating dependencies while solving a general constraint
satisfaction problem, and a method for verfying the compatibility of a
behavior with a model across time.  The algorithm can diagnose
multiple faults and uses models of faulty behavior, or behavioral
modes.<p>

We then demonstrate these techniques using an implemented program
called QDOCS and test it on some realistic problems.  Through our
experiments with a model of the reaction control system (RCS) of the
space shuttle and with a level-controller for a reaction tank, we show
that QDOCS demonstrates the best balance of generality, accuracy and
efficiency among known systems.<p>
</blockquote>

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<! ===========================================================================>

<a name="chill-dissertation-95.ps.Z"</a>

<b><li>Using Inductive Logic Programming to Automate the Construction of 
Natural Language Parsers<br></b>

John M. Zelle<br>

Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August, 1995.<p>

<blockquote>
Designing computer systems to understand natural language input is a
difficult task.  In recent years there has been considerable interest
in corpus-based methods for constructing natural language parsers.
These empirical approaches replace hand-crafted grammars with
linguistic models acquired through automated training over language
corpora.  A common thread among such methods to date is the use of
propositional or probablistic representations for the learned
knowledge.  This dissertation presents an alternative approach based
on techniques from a subfield of machine learning known as inductive
logic programming (ILP).  ILP, which investigates the learning of
relational (first-order) rules, provides an empirical method for
acquiring knowledge within traditional, symbolic parsing frameworks.<p>

This dissertation details the architecture, implementation and
evaluation of CHILL a computer system for acquiring natural
language parsers by training over corpora of parsed text.  CHILL
treats language acquisition as the learning of search-control rules
within a logic program that implements a shift-reduce parser.  Control
rules are induced using a novel ILP algorithm which handles difficult
issues arising in the induction of search-control heuristics.  Both
the control-rule framework and the induction algorithm are crucial to
CHILL's success.<p>

The main advantage of CHILL over propositional counterparts is
its flexibility in handling varied representations.  CHILL has
produced parsers for various analyses including case-role mapping,
detailed syntactic parse trees, and a logical form suitable for
expressing first-order database queries.  All of these tasks are
accomplished within the same framework, using a single, general
learning method that can acquire new syntactic and semantic categories
for resolving ambiguities.<p>

Experimental evidence from both aritificial and real-world corpora
demonstrate that CHILL learns parsers as well or better than
previous artificial neural network or probablistic approaches on
comparable tasks.  In the database query domain, which goes beyond the
scope of previous empirical approaches, the learned parser outperforms
an existing hand-crafted system.  These results support the claim that
ILP techniques as implemented in CHILL represent a viable
alternative with significant potential advantages over neural-network,
propositional, and probablistic approaches to empirical parser
construction.<p>
</blockquote>

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<! ===========================================================================>

<a name="chill-compling-95.ps.Z"</a>

<b><li>An Inductive Logic Programming Method for Corpus-based Parser
Construction<br></b>

John M. Zelle and Raymond J. Mooney<br>

Submitted to Computational Linguistics<p>

<blockquote>
In recent years there has been considerable research into corpus-based
methods for parser construction.  A common thread in this research has
been the use of propositional representations for learned knowledge.
This paper presents an alternative approach based on techniques from a
subfield of machine learning known as inductive logic programming
(ILP).  ILP, which investigates the learning of relational
(first-order) rules, provides a way of using empricial methods to
acquire knowledge within traditional, symbolic parsing frameworks.  We
describe a novel method for constructing deterministic Prolog parsers
from corpora of parsed sentences.  We also discuss several advantages
of this approach compared to propositional alternatives and present
experimental results on learning complete parsers using several
corpora including the ATIS corpus from the Penn Treebank.
</blockquote>

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<! ===========================================================================>

<a name="chill-ijcai-nll-95.ps.Z"</a>

<b><li>A Comparison of Two Methods Employing Inductive Logic Programming for
Corpus-based Parser Constuction<br></b>

John M. Zelle and Raymond J. Mooney<br>

<cite>Working Notes of the IJCAI-95 Workshop on New Approaches to
Learning for Natural Language Processing</cite>, pp.79-86, Montreal,
Quebec, August, 1995.<p>

<blockquote> 
This paper presents results from recent experiments with CHILL,
a corpus-based parser acquisition system.  CHILL treats grammar
acquisition as the learning of search-control rules within a logic
program.  Unlike many current corpus-based approaches that use
propositional or probabilistic learning algorithms, CHILL uses
techniques from inductive logic programming (ILP) to learn relational
representations.  The reported experiments compare CHILL's
performance to that of a more naive application of ILP to parser
acquisition.  The results show that ILP techniques, as employed in
CHILL, are a viable alternative to propositional methods and
that the control-rule framework is fundamental to CHILL's
success.
</blockquote>

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<a name="chill-ifoil-ml-95.ps.Z"</a>

<b><li>Inducing Logic Programs without Explicit Negative Examples<br></b>

John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney<br>

<cite>Proceedings of the Fifth International Workshop on Inductive Logic
Programming</cite>, Leuven, Belguim, Sepetember 1995.<p>


<blockquote> This paper presents a method for learning logic programs
without explicit negative examples by exploiting an assumption of
<i>output completeness</i>. A mode declaration is supplied for the
target predicate and each training input is assumed to be accompanied
by all of its legal outputs.  Any other outputs generated by an
incomplete program implicitly represent negative examples; however,
large numbers of ground negative examples never need to be generated.
This method has been incorporated into two ILP systems, CHILLIN and
IFOIL, both of which use intensional background knowledge.  Tests on
two natural language acquisition tasks, case-role mapping and
past-tense learning, illustrate the advantages of the approach.
</blockquote>

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<! ===========================================================================>

<a name="wolfie-ack-95.ps.Z"</a>

<b><li>Acquisition of a Lexicon from Semantic Representations of Sentences<br></b>

Cynthia A. Thompson<br>

<cite>33rd Annual Meeting of the Association of Computational Linguistics</cite>, pp. 335-337, Boston, MA July 1995 (ACL-95). <p>

<blockquote>
A system, WOLFIE, that acquires a mapping of words to their semantic
representation is presented and a preliminary evaluation is performed.
Tree least general generalizations (TLGGs) of the representations of input 
sentences are performed to assist in determining the representations
of individual words in the sentences.  The best guess for a meaning
of a word is the TLGG which overlaps with the highest percentage of 
sentence representations in which that word appears. 
Some promising experimental results on a non-artificial data
set are presented.
</blockquote>

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<! ===========================================================================>

<a name="foidl-jair-95.ps.Z"</a>

<b><li>Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs<br></b>

Raymond J. Mooney and Mary Elaine Califf <br>

<cite>Journal of Artificial Intelligence Research</cite>, 3 (1995) pp. 1-24.


<blockquote>
This paper presents a method for inducing logic programs from examples that
learns a new class of concepts called first-order decision lists, defined
as ordered lists of clauses each ending in a cut.  The method, called FOIDL, 
is based on FOIL but employs intensional background knowledge and avoids 
the need for explicit negative examples.  It is particularly useful for 
problems that involve rules with specific exceptions, such as learning the 
past-tense of English verbs, a task widely studied in the context of the 
symbolic/connectionist debate.  FOIDL is able to learn concise, accurate 
programs for this problem from significantly fewer examples than previous 
methods (both connectionist and symbolic).
</blockquote>

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<! ===========================================================================>

<a name="qdocs-ijcai95sub.ps.Z" </a>

<b> <li> Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes <br> </b>  

Siddarth Subramanian and Raymond J. Mooney <br>

<cite>Working Notes of the IJCAI-95 Workshop on Engneering Problems for Qualitative Reasoning</cite>, Monreal, Quebec, August 1995.<p>

<blockquote> 

This paper describes an approach to diagnosis of systems described by
qualitative differential equations represented as QSIM models.  An
implemented system QDOCS is described that performs multiple-fault,
fault-model based diagnosis, using constraint satisfaction techniques,
of qualitative behaviors of systems described by such models. We
demonstrate the utility of this system by accurately diagnosing
randomly generated faults using simulated behaviors of a portion of
the Reaction Control System of the space shuttle.

</blockquote>

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<! ===========================================================================>

<a name="misq-rt-qr-94.ps.Z"</a>

<b> <li>Learning Qualitative Models for Systems with Multiple Operating Regions<br></b>

Sowmya Ramachandran, Raymond J. Mooney and Benjamin J. Kuipers <br>

<cite>Proceedings of the Eight International Workshop of Qualitative
Reasoning about Physical Systems</cite>, pp. 212-223, Nara, Japan,
June 1994. (QR-94)

<blockquote>
The problem of learning qualitative models of physical systems from
observations of its behaviour has been addressed by several
researchers in recent years. Most current techniques limit themselves
to learning a single qualitative differential equation to model the
entire system.  However, many systems have several qualitative
differential equations underlying them.  In this paper, we present an
approach to learning the models for such systems.  Our technique
divides the behaviours into segments, each of which can be explained
by a single qualitative differential equation.  The qualitative model
for each segment can be generated using any of the existing techniques
for learning a single model.  We show that results of applying our
technique to several examples and demonstrate that it is effective.
</blockquote>

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<! ===========================================================================>

<a name="qdocs-dx-94.ps.Z" </a>

<b> <li> Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes <br> </b>  

Siddarth Subramanian and Raymond J. Mooney <br>

<cite> Working Papers of the Fifth International Workshop on
Principles of Diagnosis</cite>, pp. 321-325, New Paltz, NY, 1994. <p>

<blockquote>
This paper describes an approach to diagnosis of systems described by
qualitative differential equations represented as QSIM models.  An
implemented system QDOCS is described that performs multiple-fault,
fault-model based diagnosis, using constraint satisfaction techniques,
of qualitative behaviors of systems described by such models. We
demonstrate the utility of this system by accurately diagnosing
randomly generated faults using simulated behaviors of a portion of
the Reaction Control System of the space shuttle.
</blockquote>

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<! ===========================================================================>

<a name="assert-dissertation-94.tar.Z" </a>

<b> <li> Automatic Student Modeling and Bug Library Construction using Theory 
Refinement <br> </b>  

Paul T. Baffes <br>

Ph.D. Thesis, Department of Computer Sciences, University of Texas at
Austin, December, 1994.<p>

<blockquote>
The history of computers in education can be characterized by a
continuing effort to construct intelligent tutorial programs
which can adapt to the individual needs of a student in a
one-on-one setting. A critical component of these intelligent
tutorials is a mechanism for modeling the conceptual state of the
student so that the system is able to tailor its feedback to suit
individual strengths and weaknesses. The primary contribution of
this research is a new student modeling technique which can
automatically capture novel student errors using only correct
domain knowledge, and can automatically compile trends across
multiple student models into bug libraries. This approach has
been implemented as a computer program, ASSERT, using a machine
learning technique called theory refinement which is a method for
automatically revising a knowledge base to be consistent with a
set of examples. Using a knowledge base that correctly defines a
domain and examples of a student's behavior in that domain,
ASSERT models student errors by collecting any refinements to the
correct knowledge base which are necessary to account for the
student's behavior. The efficacy of the approach has been
demonstrated by evaluating ASSERT using 100 students tested on a
classification task using concepts from an introductory course on
the C++ programming language. Students who received feedback
based on the models automatically generated by ASSERT performed
significantly better on a post test than students who received
simple reteaching.
</blockquote>

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<! ===========================================================================>

<a name="lab-aaai-94.ps.Z" </a>

<b> <li> Inductive Learning For Abductive Diagnosis <br> </b>  

Cynthia A. Thompson and Raymond J. Mooney <br>

<cite> Proceedings of the Twelfth National Conference on
AI</cite>, pp. 664-669, Seattle, WA, July 1994. (AAAI-94) <p>

<blockquote> A new inductive learning system, LAB (Learning for
ABduction), is presented which acquires abductive rules from a set of
training examples.  The goal is to find a small knowledge base which,
when used abductively, diagnoses the training examples correctly and
generalizes well to unseen examples.  This contrasts with past systems
that inductively learn rules that are used deductively.  Each training
example is associated with potentially multiple categories
(disorders), instead of one as with typical learning systems.  LAB
uses a simple hill-climbing algorithm to efficiently build a rule base
for a set-covering abductive system.  LAB has been experimentally
evaluated and compared to other learning systems and an expert
knowledge base in the domain of diagnosing brain damage due to stroke.
</blockquote>

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<! ===========================================================================>

<a name="rapture-ml-94.ps.Z" </a>

<b> <li> Comparing Methods For Refining Certainty Factor Rule-Bases </b> <br> 

J. Jeffrey Mahoney and Raymond J. Mooney <br> 

<cite> Proceedings of the Eleventh International Workshop
on Machine Learning</cite>, pp. 173-180, Rutgers, NJ, July 1994. (ML-94) <p>

<blockquote>
This paper compares two methods for refining uncertain knowledge bases using
propositional certainty-factor rules.  The first method, implemented in the
RAPTURE system, employs neural-network training to refine the certainties
of existing rules but uses a symbolic technique to add new rules.  The second
method, based on the one used in the KBANN system, initially adds a
complete set of potential new rules with very low certainty and allows
neural-network training to filter and adjust these rules.  Experimental results
indicate that the former method results in significantly faster training and
produces much simpler refined rule bases with slightly greater accuracy.
</blockquote>

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<! ===========================================================================>

<a name="rapture-isiknh-94.ps.Z" </a>

<b> <li> Modifying Network Architectures For Certainty-Factor Rule-Base Revision
</b> <br>  
J. Jeffrey Mahoney and Raymond J. Mooney <br> 

<cite> Proceedings of the International Symposium on Integrating
Knowledge and Neural Heuristics 1994</cite>, pp. 75-84, Pensacola, FL,
May 1994. (ISIKNH-94) <p>

<blockquote> 
This paper describes RAPTURE --- a system for revising
probabilistic rule bases that converts symbolic rules into a
connectionist network, which is then trained via connectionist
techniques.  It uses a modified version of backpropagation to refine
the certainty factors of the rule base, and uses ID3's
information-gain heuristic (Quinlan) to add new rules.  Work is
currently under way for finding improved techniques for modifying
network architectures that include adding hidden units using the
UPSTART algorithm (Frean).  A case is made via comparison with fully
connected connectionist techniques for keeping the rule base as close
to the original as possible, adding new input units only as needed.
</blockquote>

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<! ===========================================================================>

<a name="chill-ml-94.ps.Z" </a>

<li> <b> Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming
</b> <br>
John M. Zelle, Raymond J. Mooney and Joshua B. Konvisser <br>

<cite> Proceedings of the Eleventh International Workshop
on Machine Learning</cite>, pp. 343-351, Rutgers, NJ, July 1994. (ML-94) <p>

<blockquote>
This paper describes a new method for inducing logic programs from
examples which attempts to integrate the best aspects of existing ILP
methods into a single coherent framework.  In particular, it combines
a bottom-up method similar to GOLEM with a top-down method similar to
FOIL.  It also includes a method for predicate invention similar to
CHAMP and an elegant solution to the ``noisy oracle'' problem which
allows the system to learn recursive programs without requiring a
complete set of positive examples.  Systematic experimental
comparisons to both GOLEM and FOIL on a range of problems are used to
clearly demonstrate the advantages of the approach.
</blockquote>

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<! ===========================================================================>

<a name="chill-aaai-94.ps.Z" </a>

<b> <li> Inducing Deterministic Prolog Parsers From Treebanks:
A Machine Learning Approach </b> <br>  

John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the Twelfth National Conference on
AI</cite>, pp. 748-753, Seattle, WA, July 1994. (AAAI-94) <p>
 
<blockquote>
This paper presents a method for constructing deterministic, context-sensitive,
Prolog parsers from corpora of parsed sentences. Our approach uses recent
machine learning methods for inducing Prolog rules from examples (inductive
logic programming).  We discuss several advantages of this method compared to
recent statistical methods and present results on learning complete parsers
from portions of the ATIS corpus.
</blockquote>

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<a name="ilp-ebl-sigart-94.ps.Z" </a>

<b> <li> Integrating ILP and EBL </b> <br>  

Raymond J. Mooney and John M. Zelle <br> 

<cite> SIGART Bulletin</cite>, Volume 5, number 1, Jan. 1994, pp 12-21. <p>

<blockquote>
This paper presents a review of recent work that integrates methods from
Inductive Logic Programming (ILP) and Explanation-Based Learning (EBL).  ILP
and EBL methods have complementary strengths and weaknesses and a number of
recent projects have effectively combined them into systems with better
performance than either of the individual approaches. In particular, integrated
systems have been developed for guiding induction with prior knowledge
(ML-SMART, FOCL, GRENDEL) refining imperfect domain theories
(FORTE, AUDREY, Rx), and learning effective search-control
knowledge (AxA-EBL, DOLPHIN).
</blockquote>

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<a name="neither-informatica-94.ps.Z" </a>

<b> <li> Extending Theory Refinement to M-of-N Rules </b> <br> 

Paul T. Baffes and Raymond J. Mooney <br> 

<cite> Informatica</cite>, 17 (1993), pp. 387-397. <p>

<blockquote>
In recent years, machine learning research has started addressing a problem
known as <em> theory refinement</em>. The goal of a theory refinement learner is
to modify an incomplete or incorrect rule base, representing a domain theory,
to make it consistent with a set of input training examples. This paper
presents a major revision of the EITHER propositional theory refinement
system. Two issues are discussed. First, we show how run time efficiency can
be greatly improved by changing from a exhaustive scheme for computing
repairs to an iterative greedy method. Second, we show how to extend
EITHER to refine MofN rules. The resulting algorithm, Neither (New 
EITHER), is more than an order of magnitude faster and produces
significantly more accurate results with theories that fit the MofN
format. To demonstrate the advantages of NEITHER, we present experimental
results from two real-world domains.
</blockquote>

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<! ===========================================================================>

<a name="lab-masters-93.ps.Z" </a>

<b> <li> Inductive Learning for Abductive Diagnosis </b> <br> 

Cynthia Thompson <br> 

M.A. Thesis, Department of Computer Sciences, University of Texas at Austin, 1993. <p>

<blockquote>
A new system for learning by induction, called LAB, is presented.  LAB
(Learning for ABduction) learns abductive rules based on a set of training
examples.  Our goal is to find a small knowledge base which, when used
abductively, diagnoses the training examples correctly, in addition to
generalizing well to unseen examples.  This is in contrast to past systems,
which inductively learn rules which are used deductively.  Abduction is
particularly well suited to diagnosis, in which we are given a set of symptoms
(manifestations) and we want our output to be a set of disorders which explain
why the manifestations are present.  Each training example is associated with
potentially multiple categories, instead of one, which is the case with typical
learning systems.  Building the knowledge base requires a choice between
multiple possibilities, and the number of possibilities grows exponentially
with the number of training examples.  One method of choosing the best
knowledge base is described and implemented.  The final system is
experimentally evaluated, using data from the domain of diagnosing brain damage
due to stroke.  It is compared to other learning systems and a knowledge base
produced by an expert.  The results are promising: the rule base learned is
simpler than the expert knowledge base and rules learned by one of the other
systems, and the accuracy of the learned rule base in predicting which areas
are damaged is better than all the other systems as well as the expert
knowledge base.
</blockquote>

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<! ===========================================================================>

<a name="assert-proposal-93.ps.Z" </a>

<b> <li> Learning to Model Students: Using Theory Refinement to Detect
Misconceptions </b> <br>

Paul T. Baffes <br> 

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1993. <p>


<blockquote>
A new student modeling system called ASSERT is described which uses domain
independent learning algorithms to model unique student errors and to
automatically construct bug libraries. ASSERT consists of two learning phases.
The first is an application of theory refinement techniques for constructing
student models from a correct theory of the domain being tutored. The second
learning cycle automatically constructs the bug library by extracting common
refinements from multiple student models which are then used to bias future
modeling efforts. Initial experimental data will be presented which suggests
that ASSERT is a more effective modeling system than other induction techniques
previously explored for student modeling, and that the automatic bug library
construction significantly enhances subsequent modeling efforts.
</blockquote>

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<a name="dolphin-chill-proposal-93.ps.Z" </a>

<b> <li> Learning Search-Control Heuristics for Logic Programs:
Applications to Speedup Learning and Language Acquisition </b> <br>

John M. Zelle <br> 

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1993. <p>

<blockquote>
This paper presents a general framework, learning search-control heuristics for
logic programs, which can be used to improve both the efficiency and accuracy
of knowledge-based systems expressed as definite-clause logic programs.  The
approach combines techniques of explanation-based learning and recent advances
in inductive logic programming to learn clause-selection heuristics that guide
program execution.  Two specific applications of this framework are detailed:
dynamic optimization of Prolog programs (improving efficiency) and natural
language acquisition (improving accuracy).  In the area of program
optimization, a prototype system, DOLPHIN, is able to transform some
intractable specifications into polynomial-time algorithms, and outperforms
competing approaches in several benchmark speedup domains.  A prototype
language acquisition system, CHILL, is also described.  It is capable of
automatically acquiring semantic grammars, which uniformly incorprate syntactic
and semantic constraints to parse sentences into case-role representations.
Initial experiments show that this approach is able to construct accurate
parsers which generalize well to novel sentences and significantly outperform
previous approaches to learning case-role mapping based on connectionist
techniques.  Planned extensions of the general framework and the specific
applications as well as plans for further evaluation are also discussed.
</blockquote>

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<b> <li> Combining FOIL and EBG to Speed-Up Logic Programs </b> <br> 

John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the Thirteenth International Joint Conference on Artificial
Intelligence</cite>, pp. 1106-1111, Chambery, France, 1993. (IJCAI-93) <p>

<blockquote>
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs.  When
these control rules are incorporated into the original program,
significant speed-up may be achieved.  The algorithm is shown to be an
improvement over competing EBL approaches in several domains.
Additionally, the algorithm is capable of automatically transforming
some intractable algorithms into ones that run in polynomial time.
</blockquote>

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<b> <li> Symbolic Revision of Theories With M-of-N Rules </b> <br> 

Paul T. Baffes and Raymond J. Mooney <br> 

<cite> Proceedings of the Thirteenth International Joint Conference on Artificial
Intelligence</cite>, pp. 1135-1140, Chambery, France, 1993. (IJCAI-93) <p>

<blockquote>
This paper presents a major revision of the EITHER propositional theory
refinement system. Two issues are discussed. First, we show how run time
efficiency can be greatly improved by changing from a exhaustive scheme for
computing repairs to an iterative greedy method. Second, we show how to extend
EITHER to refine M-of-N rules. The resulting algorithm, NEITHER (New EITHER),
is more than an order of magnitude faster and produces significantly more
accurate results with theories that fit the M-of-N format. To demonstrate the
advantages of NEITHER, we present preliminary experimental results comparing it
to EITHER and various other systems on refining the DNA promoter domain theory.
</blockquote>

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<b> <li> Learning Semantic Grammars With Constructive Inductive Logic Programming </b> <br> 

John M. Zelle  and Raymond J. Mooney <br> 

<cite> Proceedings of the Eleventh National Conference of the American
Association for Artificial Intelligence</cite>, pp. 817-822,
Washington, D.C. July 1993 (AAAI-93). <p>

<blockquote>
Automating the construction of semantic grammars is a difficult and
interesting problem for machine learning.  This paper shows how the
semantic-grammar acquisition problem can be viewed as the learning of
search-control heuristics in a logic program.  Appropriate control
rules are learned using a new first-order induction algorithm that
automatically invents useful syntactic and semantic categories.
Empirical results show that the learned parsers generalize well to
novel sentences and out-perform previous approaches based on
connectionist techniques.
</blockquote>

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<b> <li> Combining Connectionist and Symbolic Learning to Refine Certainty-Factor 
Rule-Bases </b> <br> 

J. Jeffrey Mahoney and Raymond J. Mooney <br> 

<cite> Connection Science</cite>, 5 (1993), pp. 339-364. (Special issue on
Architectures for Integrating Neural and Symbolic Processing) <p>

<blockquote>
This paper describes Rapture --- a system for revising probabilistic knowledge
bases that combines connectionist and symbolic learning methods. Rapture uses
a modified version of backpropagation to refine the certainty factors of a
Mycin-style rule base and it uses ID3's information gain heuristic to add
new rules.  Results on refining three actual expert knowledge bases demonstrate
that this combined approach generally performs better than previous methods.
</blockquote>

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<b> <li> Refinement of First-Order Horn-Clause Domain Theories </b> <br>

Bradley L. Richards and Raymond J. Mooney <br>

<cite> Machine Learning</cite> 19,2 (1995), pp. 95-131. <p>

<blockquote> Knowledge acquisition is a difficult and time-consuming
task, and as error-prone as any human activity.  The task of
automatically improving an existing knowledge base using learning
methods is addressed by a new class of systems performing <i> theory
refinement</i>.  Until recently, such systems were limited to
propositional theories.  This paper presents a system, FORTE
(First-Order Revision of Theories from Examples), for refining
first-order Horn-clause theories.  Moving to a first-order
representation opens many new problem areas, such as logic program
debugging and qualitative modelling, that are beyond the reach of
propositional systems.  FORTE uses a hill-climbing approach to revise
theories.  It identifies possible errors in the theory and calls on a
library of operators to develop possible revisions.  The best revision
is implemented, and the process repeats until no further revisions are
possible.  Operators are drawn from a variety of sources, including
propositional theory refinement, first-order induction, and inverse
resolution.  FORTE has been tested in several domains including
logic programming and qualitative modelling.  </blockquote>

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<b> <li> Encouraging Experimental Results on Learning CNF </b> <br> 

Raymond J. Mooney <br> 

<cite> Machine Learning</cite> 19,1 (1995) pp. 79-92. <p>

<blockquote>
This paper presents results comparing three inductive learning systems using
different representations for concepts, namely: CNF formulae, DNF formulae, and
decision trees. The CNF learner performs surprisingly well.  Results on five
natural data sets show that it frequently trains faster and produces more
accurate and simpler concepts.  The probable explanation for its superior
performance is that the other systems are more susceptible to the replication
problem.
</blockquote>

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<b> <li> Belief Revision in the Context of Abductive Explanation </b> <br> 

Siddarth Subramanian <br> 

Technical Report AI92-179, Artificial Intelligence Lab, <br>
University of Texas at Austin, March 1991. <p>

<blockquote> This proposal presents an approach to explanation that
incorporates the paradigms of belief revision and abduction.  We
present an algorithm that combines these techniques and a system
called BRACE that is a preliminary implementation of this
algorithm. We show the applicability of the BRACE approach to a wide
range of domains including scientific discovery, device diagnosis and
plan recognition. Finally, we describe our proposals for a new
implementation, new application domains for our system and extensions
to this approach.  </blockquote>

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<b> <li> A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition 
and Diagnosis </b> <br> 

Hwee Tou Ng and Raymond J. Mooney <br> 

Submitted for journal publication.<p>

<blockquote>
A diverse set of intelligent activities, including natural language
understanding and diagnosis, requires the ability to construct
explanations for observed phenomena.  In this paper, we view
explanation as <em> abduction</em>, where an abductive explanation is a
consistent set of assumptions which, together with background
knowledge, logically entails a set of observations.  We have
successfully built a domain-independent system, ACCEL, in which
knowledge about a variety of domains is uniformly encoded in
first-order Horn-clause axioms.  A general-purpose abduction
algorithm, AAA, efficiently constructs explanations in the
various domains by caching partial explanations to avoid redundant
work.  Empirical results show that caching of partial explanations can
achieve more than an order of magnitude speedup in run time.  We have
applied our abductive system to two general tasks: plan recognition in
text understanding, and diagnosis of medical diseases, logic circuits,
and dynamic systems.  The results indicate that ACCEL is a
general-purpose system capable of plan recognition and diagnosis, yet
efficient enough to be of practical utility.
</blockquote>

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<b> <li> Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation </b> <br> 
 
Hwee Tou Ng and Raymond J. Mooney <br> 

<cite> Proceedings of the Third International Conference on Principles
of Knowledge Representation and Reasoning</cite>, pp. 499-508,
Cambridge, MA, October 1992.<p>
 
<blockquote>
While it has been realized for quite some time within AI that abduction
is a general model of explanation for a variety of tasks, there have
been no empirical investigations into the practical feasibility of a
general, logic-based abductive approach to explanation.  In this paper
we present extensive empirical results on applying a general abductive
system, ACCEL, to moderately complex problems in plan recognition
and diagnosis.  In plan recognition, ACCEL has been tested on 50
short narrative texts, inferring characters' plans from actions
described in a text.  In medical diagnosis, ACCEL has diagnosed 50
real-world patient cases involving brain damage due to stroke
(previously addressed by set-covering methods).  ACCEL also uses
abduction to accomplish model-based diagnosis of logic circuits (a full
adder) and continuous dynamic systems (a temperature controller and the
water balance system of the human kidney).  The results indicate that
general purpose abduction is an effective and efficient mechanism for
solving problems in plan recognition and diagnosis.
</blockquote>

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<b> <li> Automatic Abduction of Qualitative Models </b> <br> 

Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers <br> 

<cite> Proceedings of the Tenth National Conference on Artificial
Intelligence</cite>, pp. 723-728, San Jose, CA, July 1992. <p>

<blockquote>
We describe a method of automatically abducing qualitative models from
descriptions of behaviors.  We generate, from either quantitative or
qualitative data, models in the form of qualitative differential equations
suitable for use by QSIM.  Constraints are generated and filtered both by
comparison with the input behaviors and by dimensional analysis.  If the
user provides complete information on the input behaviors and the
dimensions of the input variables, the resulting model is unique,
maximally constrainted, and guaranteed to reproduce the input behaviors.
If the user provides incomplete information, our method will still
generate a model which reproduces the input behaviors, but the model
may no longer be unique.  Incompleteness can take several forms:  missing
dimensions, values of variables, or entire variables.
</blockquote>

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<b> <li> Learning Relations by Pathfinding </b> <br> 

Bradley L. Richards and Raymond J. Mooney <br> 

<cite> Proceedings of the Tenth National Conference on Artificial
Intelligence</cite>, pp. 50-55, San Jose, CA, July 1992. <p>

<blockquote>
First-order learning systems (e.g., FOIL, FOCL, FORTE) generally rely on
hill-climbing heuristics in order to avoid the combinatorial explosion
inherent in learning first-order concepts.  However, hill-climbing leaves
these systems vulnerable to local maxima and local plateaus.  We present
a method, called relational pathfinding, which has proven highly effective
in escaping local maxima and crossing local plateaus.  We present our
algorithm and provide learning results in two domains:  family relationships
and qualitative model building.
</blockquote>

<!WA100><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/forte-aaai-92.ps.Z">
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<b> <li> Speeding-up Logic Programs by Combining EBG and FOIL </b> <br> 
 
John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the 1992 Machine Learning Workshop on Knowledge
Compilation and Speedup Learning</cite>, Aberdeen Scotland, July 1992. <p>

<blockquote>
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs.  When these
control rules are incorporated into the original program, significant
speed-up may be achieved.  The algorithm produces not only EBL-like
speed up of problem solvers, but is capable of automatically
transforming some intractable algorithms into ones that run in
polynomial time.
</blockquote>

<!WA102><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/dolphin-mlw-92.ps.Z">
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<b> <li> Combining Symbolic and Neural Learning to Revise Probabilistic Theories </b> <br> 

J. Jeffrey Mahoney & Raymond J. Mooney <br> 

<cite> Proceedings of the 1992 Machine Learning Workshop on Integrated
Learning in Real Domains</cite>, Aberdeen Scotland, July 1992. <p>

<blockquote>
This paper describes RAPTURE --- a system for revising probabilistic
theories that combines symbolic and neural-network learning methods. 
RAPTURE uses a modified version of backpropagation to refine the certainty
factors of a Mycin-style rule-base and it uses ID3's information gain heuristic
to add new rules.  Results on two real-world domains demonstrate that this
combined approach performs as well or better than previous methods.
</blockquote>

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<b> <li> Growing Layers of Perceptrons: Introducing the Extentron Algorithm </b> <br> 

Paul T. Baffes and John M. Zelle <br> 

<cite> Proceedings of the 1992 International Joint Conference on Neural
Networks</cite>, pp. 392-397, Baltimore, Maryland, June 1992 <p>

<blockquote>
The ideas presented here are based on two observations of perceptrons: (1) when
the perceptron learning algorithm cycles among hyperplanes, the hyperplanes may
be compared to select one that gives a best <em> SPLIT </em> of the examples, and
(2) it is always possible for the perceptron to build a hyperplane that
separates <em> at least one </em> example from all the rest. We describe the
Extentron, which grows multi-layer networks capable of distinguishing
non-linearly-separable data using the simple perceptron rule for linear
threshold units. The resulting algorithm is simple, very fast, scales well to
large problems, retains the convergence properties of the perceptron, and can
be completely specified using only two parameters.  Results are presented
comparing the Extentron to other neural network paradigms and to symbolic
learning systems.
</blockquote>

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<b> <li> Using Theory Revision to Model Students and Acquire Stereotypical Errors </b> <br> 

Paul T. Baffes and Raymond J. Mooney <br> 

<cite> Proceedings of the Fourteenth Annual Conference of the Cognitive
Science Society</cite>, pp. 617-622, Bloomington, IN, July 1992. <p>

<blockquote>
Student modeling has been identified as an important component to the long
term development of Intelligent Computer-Aided Instruction (ICAI) systems. Two
basic approaches have evolved to model student misconceptions. One uses a
static, predefined library of user bugs which contains the misconceptions
modeled by the system. The other uses induction to learn student
misconceptions from scratch. Here, we present a third approach that uses a
machine learning technique called theory revision. Using theory revision
allows the system to automatically construct a bug library for use in modeling
while retaining the flexibility to address novel errors.
</blockquote>

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<b> <li> A Preliminary PAC Analysis of Theory Revision </b> <br> 

Raymond J. Mooney <br> 

<cite> Computational Learning Theory and Natural Learning Systems</cite>, 
Vol.  3, T. Petsche, S. Judd, and S. Hanson, Eds., MIT Press, 1995, pp. 43-53. <p>

<blockquote>
This paper presents a preliminary analysis of the sample complexity of theory
revision within the framework of PAC (Probably Approximately Correct)
learnability theory.  By formalizing the notion that the initial theory is
``close'' to the correct theory we show that the sample complexity of an
optimal propositional Horn-clause theory revision algorithm is $O( ( \ln 1 /
\delta + d \ln (s_0 + d + n) ) / \epsilon)$, where $d$ is the {\em syntactic
distance} between the initial and correct theories, $s_0$ is the size of
initial theory, $n$ is the number of observable features, and $\epsilon$ and
$\delta$ are the standard PAC error and probability bounds. The paper also
discusses the problems raised by the computational complexity of theory
revision.
</blockquote>

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<b> <li> Automated Debugging of Logic Programs via Theory Revision </b> <br> 

Raymond J. Mooney & Bradley L. Richards <br> 

<cite> Proceedings of the Second International Workshop on Inductive
Logic Programming</cite>, Tokyo, Japan, June 1992. <p>

<blockquote>
This paper presents results on using a theory revision system to automatically
debug logic programs. FORTE is a recently developed system for revising
function-free Horn-clause theories.  Given a theory and a set of training
examples, it performs a hill-climbing search in an attempt to minimally modify
the theory to correctly classify all of the examples.  FORTE makes use of
methods from propositional theory revision, Horn-clause induction (FOIL),
and inverse resolution.  The system has has been successfully used to debug
logic programs written by undergraduate students for a programming languages
course.
</blockquote>

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<b> <li> Batch versus Incremental Theory Refinement </b> <br> 

Raymond J. Mooney <br> 

<cite> Proceedings of AAAI Spring Symposium on Knowledge
Assimilation</cite>, Standford, CA, March, 1992. <p>

<blockquote>
Most existing theory refinement systems are not incremental. However, any
theory refinement system whose input and output theories are compatible can be
used to incrementally assimilate data into an evolving theory.  This is done by
continually feeding its revised theory back in as its input theory.  An
incremental batch approach, in which the system assimilates a batch of examples
at each step, seems most appropriate for existing theory revision systems.
Experimental results with the EITHER theory refinement system demonstrate
that this approach frequently increases efficiency without significantly
decreasing the accuracy or the simplicity of the resulting theory.  However, if
the system produces bad initial changes to the theory based on only small
amount of data, these bad revisions can ``snowball'' and result in an overall
decrease in performance.
</blockquote>

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<b> <li> A Multistrategy Approach to Theory Refinement </b> <br> 

Raymond J. Mooney & Dirk Ourston <br> 

<cite> Machine Learning: A Multistrategy Approach</cite>, Vol. IV, R.S. Michalski
& G. Teccuci (eds.), pp.141-164, Morgan Kaufman, San Mateo, CA, 1994. <p>

<blockquote>
This chapter describes a multistrategy system that employs independent modules
for deductive, abductive, and inductive reasoning to revise an arbitrarily
incorrect propositional Horn-clause domain theory to fit a set of preclassified
training instances.  By combining such diverse methods, EITHER is able
to handle a wider range of imperfect theories than other theory revision
systems while guaranteeing that the revised theory will be consistent with the
training data.  EITHER has successfully revised two actual expert
theories, one in molecular biology and one in plant pathology. The results
confirm the hypothesis that using a multistrategy system to learn from both
theory and data gives better results than using either theory or data alone.
</blockquote>

<!WA116><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/either-bkchapter-94.ps.Z">
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<a name="iou-either-bkchapter-93.ps.Z" </a>

<b> <li> Integrating Theory and Data in Category Learning </b> <br> 

Raymond J. Mooney <br> 

<cite> Categorization by Humans and Machines: The Psychology of Learning
and Motivation</cite>, Vol. 29, G. Nakamura, R. Taraban, & D.L. Medin (Eds.),
pp. 189-218, Academic Press, Orlando, FL, 1993. <p>

<blockquote>
Recent results in both machine learning and cognitive psychology demonstrate
that effective category learning involves an integration of theory and data.
First, theories can bias induction, affecting what category definitions are
extracted from a set of examples. Second, conflicting data can cause theories
to be revised.  Third, theories can alter the representation of data through
feature formation.  This chapter reviews two machine learning systems
that attempt to integrate theory and data in one or more of these ways.  IOU
uses a domain theory to acquire part of a concept definition and to focus
induction on the unexplained aspects of the data.  EITHER uses data to
revise an imperfect theory and uses theory to add abstract features to the
data.  Recent psychological experiments reveal that these machine learning
systems exhibit several important aspects of human category learning.
Specifically, IOU has been used to successfully model some recent experimental
results on the effect of functional knowledge on category learning.
</blockquote>

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<b> <li> Theory Refinement Combining Analytical and Empirical Methods </b> <br> 

Dirk Ourston and Raymond J. Mooney <br> 

<cite> Artificial Intelligence</cite>, 66 (1994), pp. 311--344. <p>

<blockquote>
This article describes a comprehensive approach to automatic theory revision.
Given an imperfect theory, the approach combines explanation attempts for
incorrectly classified examples in order to identify the failing portions of
the theory. For each theory fault, correlated subsets of the examples are used
to inductively generate a correction. Because the corrections are <em>
focused</em>, they tend to preserve the structure of the original theory.  Because
the system starts with an approximate domain theory, in general fewer training
examples are required to attain a given level of performance (classification
accuracy) compared to a purely empirical system. The approach applies to
classification systems employing a propositional Horn-clause theory. The system
has been tested in a variety of application domains, and results are presented
for problems in the domains of molecular biology and plant disease diagnosis.
</blockquote>

<!WA120><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/either-aij-94.ps.Z">
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<b> <li> Induction Over the Unexplained: Using Overly-General Domain Theories to Aid
Concept Learning </b> <br> 

Raymond J. Mooney <br> 

<cite>Machine Learning</cite>, 10, 1 (1993), pp. 79-110. <p>

<blockquote>
This paper describes and evaluates an approach to combining empirical and
explanation-based learning called Induction Over the Unexplained (IOU).
IOU is intended for learning concepts that can be partially explained by an
overly-general domain theory. An eclectic evaluation of the method is presented
which includes results from all three major approaches: empirical, theoretical,
and psychological.  Empirical results shows that IOU is effective at refining
overly-general domain theories and that it learns more accurate concepts from
fewer examples than a purely empirical approach.  The application of
theoretical results from PAC learnability theory explains why IOU requires
fewer examples.  IOU is also shown to be able to model psychological data
demonstrating the effect of background knowledge on human learning.
</blockquote>

<!WA122><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/iou-nlj-93.ps.Z">
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<a name="accel-aaai-91.ps.Z" </a>

<b> <li> An Efficient First-Order Horn-Clause Abduction System Based on the ATMS </b> <br> 

Hwee Tou Ng and Raymond J. Mooney <br> 

<cite>Proceedings of the Ninth National Conference on
Artificial Intelligence</cite>, pages 494-499, Anaheim, CA, July 1991. <p>

<blockquote>
This paper presents an algorithm for first-order Horn-clause abduction
that uses an ATMS to avoid redundant computation.  This algorithm is
either more efficient or more general than any other previous
abduction algorithm.  Since computing all minimal abductive
explanations is intractable, we also present a heuristic version of
the algorithm that uses beam search to compute a subset of the
simplest explanations.  We present empirical results on a broad range
of abduction problems from text understanding, plan recognition, and
device diagnosis which demonstrate that our algorithm is at least an
order of magnitude faster than an alternative abduction algorithm that
does not use an ATMS.
</blockquote>

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<b> <li> Improving Shared Rules in Multiple Category Domain Theories </b> <br> 

Dirk Ourston and Raymond J. Mooney <br> 

<cite> Proceedings of the Eighth International Machine Learning
Workshop</cite>, pp. 534-538, Evanston, IL, June 1991. <p>

<blockquote>
This paper presents an approach to improving the classification performance of
a multiple category theory by correcting intermediate rules which are shared
among the categories.  Using this technique, the performance of a theory in one
category can be improved through training in an entirely different category.
Examples of the technique are presented and experimental results are given.
</blockquote>

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<b> <li> Constructive Induction in Theory Refinement </b> <br> 

Raymond J. Mooney and Dirk Ourston <br> 

<cite> Proceedings of the Eighth International Machine Learning
Workshop</cite>, pp. 178-182, Evanston, IL. June 1991. <p>

<blockquote>
This paper presents constructive induction techniques recently added to the
EITHER theory refinement system.  These additions allow EITHER to handle
arbitrary gaps at the ``top,'' ``middle,'' and/or ``bottom'' of an incomplete
domain theory.  <i> Intermediate concept utilization</i> employs existing rules
in the theory to derive higher-level features for use in induction.  <i>
Intermediate concept creation</i> employs inverse resolution to introduce new
intermediate concepts in order to fill gaps in a theory that span multiple
levels.  These revisions allow EITHER to make use of imperfect domain theories
in the ways typical of previous work in both constructive induction and theory
refinement.  As a result, EITHER is able to handle a wider range of theory
imperfections than does any other existing theory refinement system.
</blockquote>

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<b> <li> Theory Refinement with Noisy Data </b> <br> 

Raymond J. Mooney and Dirk Ourston <br> 

Technical Report AI 91-153, Artificial Intelligence Lab, University of
Texas at Austin, March 1991. <p>

<blockquote>
This paper presents a method for revising an approximate domain theory based on
noisy data. The basic idea is to avoid making changes to the theory that
account for only a small amount of data. This method is implemented in the
EITHER propositional Horn-clause theory revision system.  The paper
presents empirical results on artificially corrupted data to show that this
method successfully prevents over-fitting.  In other words, when the data is
noisy, performance on novel test data is considerably better than revising the
theory to completely fit the data. When the data is not noisy, noise processing
causes no significant degradation in performance.  Finally, noise processing
increases efficiency and decreases the complexity of the resulting theory.
</blockquote>

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<b> <li> On the Role of Coherence in Abductive Explanation </b> <br> 

Hwee Tou Ng and Raymond J. Mooney <br> 

<cite>Proceedings of the Eighth National Conference on 
Artificial Intelligence</cite>, pages 337-342, Boston, MA, 1990. <p>

<blockquote>
Abduction is an important inference process underlying much of human
intelligent activities, including text understanding, plan
recognition, disease diagnosis, and physical device diagnosis.  In
this paper, we describe some problems encountered using abduction to
understand text, and present some solutions to overcome these
problems. The solutions we propose center around the use of a
different criterion, called <em> explanatory coherence</em>, as the
primary measure to evaluate the quality of an explanation.  In
addition, explanatory coherence plays an important role in the
construction of explanations, both in determining the appropriate
level of specificity of a preferred explanation, and in guiding the
heuristic search to efficiently compute explanations of sufficiently
high quality.
</blockquote>

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<address><!WA134><a href="http://www.cs.utexas.edu/users/estlin/">estlin@cs.utexas.edu</a></address>
